我正在尝试将.arff文件传递给LinearRegression对象，这样做时它给了我这个异常无法处理多值名义类！.
实际发生的是我正在使用CFSSubsetEval评估器执行属性选择，并在这样做之后搜索为GreedyStepwise，将这些属性传递给 LinearRegression，如下所示
LinearRegression rl=new LinearRegression(); rl.buildClassifier(data);
data 是 Instance 对象，它具有来自 .arff 文件的数据，该文件之前仅使用 weka 转换为标称值。我在这里做错什么了吗？我试图在谷歌上搜索这个错误，但找不到。
代码
package com.attribute;
import java.io.BufferedReader;
import java.io.FileReader;
import java.util.Random;
import weka.attributeSelection.AttributeSelection;
import weka.attributeSelection.CfsSubsetEval;
import weka.attributeSelection.GreedyStepwise;
import weka.classifiers.Evaluation;
import weka.classifiers.functions.LinearRegression;
import weka.classifiers.meta.AttributeSelectedClassifier;
import weka.classifiers.trees.J48;
import weka.core.Instances;
import weka.core.Utils;
import weka.filters.supervised.attribute.NominalToBinary;
/**
* performs attribute selection using CfsSubsetEval and GreedyStepwise
* (backwards) and trains J48 with that. Needs 3.5.5 or higher to compile.
*
* @author FracPete (fracpete at waikato dot ac dot nz)
*/
public class AttributeSelectionTest2 {
/**
* uses the meta-classifier
*/
protected static void useClassifier(Instances data) throws Exception {
System.out.println("\n1. Meta-classfier");
AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();
CfsSubsetEval eval = new CfsSubsetEval();
GreedyStepwise search = new GreedyStepwise();
search.setSearchBackwards(true);
J48 base = new J48();
classifier.setClassifier(base);
classifier.setEvaluator(eval);
classifier.setSearch(search);
Evaluation evaluation = new Evaluation(data);
evaluation.crossValidateModel(classifier, data, 10, new Random(1));
System.out.println(evaluation.toSummaryString());
}
/**
* uses the low level approach
*/
protected static void useLowLevel(Instances data) throws Exception {
System.out.println("\n3. Low-level");
AttributeSelection attsel = new AttributeSelection();
CfsSubsetEval eval = new CfsSubsetEval();
GreedyStepwise search = new GreedyStepwise();
search.setSearchBackwards(true);
attsel.setEvaluator(eval);
attsel.setSearch(search);
attsel.SelectAttributes(data);
int[] indices = attsel.selectedAttributes();
System.out.println("selected attribute indices (starting with 0):\n"
+ Utils.arrayToString(indices));
useLinearRegression(indices, data);
}
protected static void useLinearRegression(int[] indices, Instances data) throws Exception{
System.out.println("\n 4. Linear-Regression on above selected attributes");
BufferedReader reader = new BufferedReader(new FileReader(
"C:/Entertainement/MS/Fall 2014/spdb/project 4/healthcare.arff"));
Instances data1 = new Instances(reader);
data.setClassIndex(data.numAttributes() - 1);
/*NominalToBinary nb = new NominalToBinary();
for(int i=0;i<=20; i++){
//Still coding left here, create an Instance variable to store the data from 'data' variable for given indices
Instances data_lr=data1.
}*/
LinearRegression rl=new LinearRegression(); //Creating a LinearRegression Object to pass data1
rl.buildClassifier(data1);
}
/**
* takes a dataset as first argument
*
* @param args
* the commandline arguments
* @throws Exception
* if something goes wrong
*/
public static void main(String[] args) throws Exception {
// load data
System.out.println("\n0. Loading data");
BufferedReader reader = new BufferedReader(new FileReader(
"C:/Entertainement/MS/Fall 2014/spdb/project 4/healthcare.arff"));
Instances data = new Instances(reader);
if (data.classIndex() == -1)
data.setClassIndex(data.numAttributes() - 14);
// 1. meta-classifier
useClassifier(data);
// 2. filter
//useFilter(data);
// 3. low-level
useLowLevel(data);
}
}
注意：由于我没有编写代码来构建具有“索引”属性的实例变量，我（为了程序运行）从同一个原始文件加载数据。
我不知道如何为示例数据上传文件，但它看起来像这样。[链接]（https://scontent-a-dfw.xx.fbcdn.net/hphotos-xfa1/t31.0-8/p552x414/10496920_756438941076936_8448023649960186530_o.jpg）
